The Effect of State-Level Social Distancing Policy Stringency Measures (SDPS) on Mobility in the States of Brazil

The Effect of State-Level Social Distancing Policy Stringency Measures
(SDPS) on Mobility in the States of Brazil

Lorena G. Barberia ¹

Luiz G. R. Cantarelli ¹

Maria Leticia Claro de Faria Oliveira ¹

Natália de Paula Moreira ¹
Isabel Seelaender Costa Rosa ¹

¹ Universidade de São Paulo / Department of Political Science, São Paulo / SP – Brazil

 There has been a wide range of responses by governments to the COVID-19 (coronavirus)
 pandemic. In Brazil’s case, sub-national governments have played a particularly important
 role as the key actors implementing non-pharmaceutical interventions to halt the spread of
 the virus. Building on the methodology proposed by the Oxford COVID-19 Government
 Response Tracker (OxCGRT), we coded the stringency levels of state-level school, commerce
 and services, public gathering and private event closure policies and describe these actions’
 duration at the state-level in Brazil from early February to mid-May 2020. Our results suggest
 significant heterogeneity across Brazil and within states in social distancing policy stringency
 across this period. We then apply dynamic times-series cross-sectional methods to evaluate
 the effect of anti-contagion policies on the population’s mobility, measured using cell phone
 location data. Even though the adhesion to social distancing across Brazil’s states was
 relatively moderate compared to states in other countries during the same period of the
 pandemic, we find that anti-contagion policies had a significant effect on producing higher
 adherence to remaining at home. Nevertheless, our results also suggest that social distancing
 policies have a greater impact when a more complete and coherent set of policies were
 introduced and sustained by state governments.
 Keywords: COVID-19; non-pharmaceutical interventions; Brazil; states; federalism.

 Os efeitos ao nível estadual da Medida de Rigidez das Políticas de Distanciamento Social
 (SPDS) na Mobilidade nos Estados Brasileiros
 Uma série de respostas foram apresentadas pelos governos nos esforços de combate à
 pandemia COVID-19. No caso brasileiro, os governos subnacionais exerceram um papel
 particularmente importante como os atores-chave na implementação de medidas não

Article received on June 01, 2020 and accepted on November 25, 2020.
[Original version]
 1

farmacêuticas para contenção do alastramento do vírus. Baseando-se no Oxford COVID-19
 Government Response Tracker (OxCGRT), codificamos a rigidez das políticas de
 fechamento de escolas, comércio e serviços, aglomerações públicas e eventos privados
 implementadas ao nível estadual e descrevemos sua duração nos estados brasileiros entre
 meados de fevereiro e a metade de maio de 2020. Nossos resultados sugerem alta
 heterogeneidade ao longo do país e dentro dos estados na rigidez das políticas de
 distanciamento social neste período. Aplicamos em seguida métodos dinâmicos de séries
 temporais em painel com o objetivo de avaliar o efeito das políticas de combate ao contágio
 na mobilidade da população, medida através da utilização de dados de localização
 provenientes de celulares. Apesar da adesão relativamente moderada às medidas de
 distanciamento social pelos estados brasileiros, em comparação a estados em outros países
 durante o mesmo período, nossos achados sugerem que as políticas de combate à
 contaminação tiveram efeito significante na produção de níveis mais altos de adesão à
 permanência no lar. Adicionalmente, nossos resultados sugerem que políticas de
 distanciamento social tiveram maior impacto quando conjuntos de medidas coerentes e
 completos foram introduzidos e mantidos pelos governos estaduais.
 Palavras-chave: COVID-19; intervenções não farmacêuticas; Brasil; estados; federalismo.

ACKNOWLEDGMENTS
The additional authors of this study that were not listed due to space limitations include Pedro
Schmalz, Marcela Zamudio and Fabiana Pereira. We are grateful for the comments from
members of the Rede de Pesquisa Solidária em Polı́ticas Pú blicas e Sociedade, the Observató
rio COVID-19 Br, and the Quantitative Methods Research Group in the Department of Political
Science, and colleagues at the University of São Paulo, and most especially to Guy D. Whitten.
Lorena Barberia thanks the Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP)
for research grant support (2018/20853-1 and 2013/15658-1). Maria Letıcia Claro is sup- ported
by a grant from CNPq (132580/2019-5). Luiz Guilherme Cantarelli is supported by a grant from
CAPES (88887.355183/2019-00). Natália de Paula Moreira thanks the Fundação de Amparo a
Pesquisa do Estado de São Paulo (FAPESP) for research grant support 2016/13199-8. All errors
are our own.

1. INTRODUCTION

Governments across the world have responded rapidly and with an unprecedented wide-range of
measures to address the spread of a coronavirus, which was first detected in China and reported

 2

to the WHO in December 2019.1 The virus (abbreviated “COVID-19”) was declared a pandemic
by the World Health Organization (WHO) on March 11, 2020 (World Health Organisation, 2020)
and was detected in most countries by early March. A range of policies have been adopted by
most nations (Hsiang et al., 2020; Kim & Castro, 2020; Maier & Brockmann, 2020). These policies
have included non-pharmaceutical interventions to create social distancing, augment public health
provision, and fiscal and monetary policies to sustain economic activity. There has been
significant scholarship directed at assessing the effectiveness of these policies (Niud & Xu, 2020;
Rader et al., 2020).
 As one of the world’s most populous democracies encompassing roughly half of South
America, the COVID pandemic struck Brazil, a country with over 200 million inhabitants, as it
continued to face political and economic duress stemming from the aftermath of a protracted
regression that began in 2014 (Barbosa, 2017) and political polarization stemming from the
impeachment of president Dilma Rouseff in 2016 and the election of Jair Bolsonaro in 2018
(Abrucio, Grin, Franzese, Segatto & Couto, 2020). In this context, the risks posed by the
coronavirus were especially high for millions of people who live in poverty or have only recently
emerged from it and due to the limited infrastructure that exists for those requiring hospital care
(Rache et al., 2020). Since the first confirmed case in Brazil occurred on February 25, 2020
(Candido et al., 2020), the epicenter of COVID-19 infection rates remained concentrated in the
State of São Paulo up until mid-May 2020 (Ministry of Health, 2020). The coronavirus pandemic
struck many of the states of Brazil, and although the proportion of infections was still low in some
states by early May, other states experienced a rapid increase in infections and the collapse of their
hospital system due to the sudden surge in patients (Orellana, Cunha, Marrero, Horta & Leite,
2020).2
 As we document in this paper, sub-national governments played a particularly important
role as the key actors implementing social distancing measures to halt the spread of the virus in
Brazil (Barberia et al., 2020b). Building on Oxford COVID-19 Government Response Tracker
(OxCGRT) (Hale et al. 2020), we show how the stringency of government policy responses to
COVID-19 can be used to better understand how state governments deterred COVID-19 in the
context of federalism in a middle-income country. In this sense, our study contributes to a series
of recent studies that have aimed to examine how sub-national policies affect mobility (Jay et al.
2020), cases and hospitalizations (Courtemanche, Garuccio, Le, Pinkston & Yelowitz, 2020;

1
 The Chinese government first reported to the WHO Country Office in China a pneumonia of unknown cause detected
in Wuhan, China on December 31, 2019.
2
 We are well aware that there are reasons to suspect that these low figures may be because testing was limited to
severely ill patients in the early months of the pandemic, as well as other problems that may affect why the official
rate of confirmed cases may be lower than the actual, or ”true” rate (Li et al. 2020).
 3

Hsiang et al., 2020), and how sub-national political dynamics impact policy adoption distinctly
in federations (Adolph, Amano, Bang-Jensen, Fullman & Wilkerson, 2020). Unlike these studies,
which mainly focus on developed economies in North America and Europe, we explore dynamics
in Latin America’s largest federation.
 After introducing the (COVID-19 Government Response Tracker for the Brazilian
Federation [CGRT-BRFED]) (Barberia et al., 2020a), the remaining sections of this study
evaluate whether the variations in the social distancing policies adopted by the 26 states of the
Brazilian federation and the federal district since the onset of the pandemic led to different rates
of mobility by citizens residing in these states. Using the CGRT-BRFED, which details the school,
work, and large, public gathering closure policies that have been enacted and the duration of these
actions at the state level in Brazil, we test whether state-level Social Distancing Policy Stringency
policies impacted the movement of the population.3
 Our results suggest that social distancing policies had a greater impact on reducing
mobility when a more complete and rigorous set of policies were adopted. As a robustness check,
further tests were conducted to verify if these results were robust to national political dynamics.
Specifically, our results show that state-level Social Distancing Policy Stringency remained
relevant to explaining population mobility holding constant President Bolsonaro’s televised
presidential addresses and key political events, such as the firing of Health Minister Luiz
Mandetta, on April 16th, 2020, and the resignation of Nelson Teich, the second Health Minister
of the Bolsonaro government, on May 15th, 2020.

2. DATA AND METHODS

To slow the spread of the virus, a broad range of policies have been enacted by governments to
increase the physical distance between people. These policies have been commonly referred to as
non-pharmaceutical interventions. To contain the spread of the virus’s transmission,
approximately three weeks after the first confirmed case in the country, the primary response in
the early stages of the pandemic to enact social distancing policies was made by state governments
in mid-March 2020. These actions were made possible because the authority to adopt social
distancing policies was given to sub-national governments by the Federal government in early
February4and subsequently affirmed by the Supreme Court5. In the early stages, state policies also

3
 These indicators are being included in the original Oxford COVID-19 Government Response Tracker (OxCGRT)
to code the sub-national policies of Brazil in collaboration with scholars from Oxford and the Fundação Getulio
Vargas.
4
 Retrieved from http://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/lei/l13979.htm
5
 Retrieved from http://www.stf.jus.br/arquivo/cms/noticiaNoticiaStf/anexo/ADI6341.pdf
 4

tended to supersede local municipal policies because although the responsibility for adopting
measures against COVID-19 in the Brazilian federation is concurrently shared between federal,
state, and city governments, Supreme Court rulings affirmed that lower levels of government could
not adopt less stringent policies than those adopted by higher- level governments.
 Similar to the original Oxford COVID-19 Government Response Tracker (OxCGRT) (Hale
et al., 2020) indicators, the CGRT-BRFED uses publicly available information to code the
stringency measures adopted by the federal and 26 state governments and the federal district of
Brasilia. The novelty of the CGRT-BRFED is that it emphasizes that a wide range of government
policies are being enacted by levels of government (federal, state, and municipal). We coded
policies as prescribed in government decrees, laws, and other official rulings published in
government official gazettes for each policy. Our thorough approach to search these official
government sources reduces measurement error. For each policy, the length of the policy was
recorded. The time-series cross- section data set consists of 145 (February 2nd to May 23rd, 2020)
observations for each state (26 units) and the Federal District of Brasilia.6
 As an airborne virus, measures to increase social distancing have been enacted to reduce
physical and social contact and ensure individuals’ physical distance. These measures are deemed
to be particularly important in public spaces where many people frequent facilities (e.g., schools,
workplaces in city centers) or gatherings that assemble large crowds (e.g., church, concerts,
cinemas, shopping centers, etc.). Table 1 describes the closure policies for: schools (SDP1), the
commerce and service sectors (SDP2A), industry (SDP2B), public gatherings (SPD3), and private
events (SDP4)7.
 Following the OxCGRT coding, policy decisions regarding social distancing for variables
SDP1, SDP2A, and SDP2B receive a score of 1 (one) if they recommend closures, 2 (two) if the
closure is mandatory, but non-essential services are permitted to operate within the state’s
jurisdiction, and 3 (three) if the suspension becomes mandatory for all levels and sectors. In case
there are no closure mandates in place, a score 0 (zero) was assigned. We consider non-essential
services as referring to activities that are not specifically related to either the health or food
sectors. For the public gatherings (SDP3), policies are scored from 0 (zero) to 2 (two). Lastly,
private gatherings and events (SPD4) receive a score of 1 (one) when there is a restriction on
gatherings of over 1000 people, 2 (two) if there are restrictions on gatherings between 101-1000

6
 Following the OxCGRT, the indicators of the CGRT-BRFED were scored for each day starting on January 1st,
2020. This is well in advance of the onset of the first confirmed cases of COVID-19 in Brazil. In this study, we focus
on the data from February 2nd to May 23rd. By February 1st, the WHO had already declared a health emergency,
and cases had been detected in several countries.
7
 In the OxCGRT, overall work closures are recorded. As we are interested in more granular-level diagnostics at the
local level, the CGRT-BRFED further desegregates economic activity to distinguish whether states restricted
commercial, service-sector, and industrial activities.
 5

people, 3 (three) if there are restrictions on gatherings between 11-100 people, and 4 (four) if
there are restrictions on gatherings of up to 10 people. Furthermore, each policy is also coded for
its geographical coverage. If the closure policies are directed at the entire state, these policies
receive a geographical target score of 1 (one), and if the policy are geographically targeted for
specific hot spots a score of 0 (zero) is assigned. Finally, each policy receives a combined score,
considering the two dimensions. Additionally, the four social distancing indicators were then
summed to get a composite Social Distancing Policy Stringency measure, and this measure was
re-scaled to create a composite Social Distancing Policy Stringency (SDPS) Index that ranges
from 0 (no policy intervention) to 100 (maximum level of intervention) 8. As we carefully
inspected all government decrees and therefore clearly identified mandated versus recommended
closures as regulated in laws, there are no missing data for these indicators in the CGRT-BRFED
data set9.
 There are other approaches that have been employed to evaluate social distancing policies
and how they affect the pandemic. For example, Hsiang et al. (2020) emphasize the discrete effect
of the deployment of these policies. Since our goal in this paper is to measure the intensity of the
social distancing policies on compliance, the SDPS - as an aggregate index that measures the
intensity of the combined stringency policies- is well suited to the purpose of this study.
 To capture the extent to which citizens in different states comply with social distancing policies, we
use different measures of location mobility based on aggregated data from the location analysis company
InLoco, which tracks about 60 million smart- phone cellphone users nation-wide daily (Ferraz 2020). InLoco
provided us with data based on three measures. The first measure is the percentage of cellphones at the same
geographical location as they were during work hours as they were in non-work hours (10 p.m. to 6 a.m.).
The second measure is the change in the average number of kilometers traveled on a specific day in each state
relative to the first five weeks of the year. The third measure is the change in the average percentage of non-
essential trips made on each specific day in each state relative to the first five weeks of the year. It should be
noted that these data are regarded as highly precise to location as the estimated standard error has been
calculated to be 2.8 meters. The data are aggregate measures of overall mobility each day in each state.

8
 As part of our project to map public policies, we expect to make future changes to further refine the coding and
overall Social Distancing Policy Stringency Index. We also plan to make further refinements to address geographic
implications. These changes and new indicators will be posted at: https://github.com/cgrtbrfed/covid19policytracker.
9
 Additional policies targeting social distancing that do not refer to closures or activity suspension were not analyzed
in this study. We recognize the importance of these measures in combating the COVID- 19 pandemic. Nevertheless,
we decided to restrict our analysis to analyze the intensity of closures’ intensity on mobility.
 6

Table 1
 Social Distancing Policies and Social Distancing Policy Stringency (SDPS) Index

Indicator Name General Geographically-Targeted

 0 = No measure taken;
SDP1 School Closures 1 = Suspension or cancellation recommended 0 = Geographically-targeted
 2 = Suspension or cancellation enacted, but sector-targeted 3 = Suspension or 1 = State-wide
 cancellation enacted, all levels
 0 = No measure taken;
SDP2A Commerce and Service-sector Closures
 1 = Suspension or cancellation recommended
 0 = Geographically-targeted
 2 = Suspension or cancellation enacted, but sector-targeted 3 = Suspension or
 1 = State-wide
 cancellation enacted, all levels
 0 = No measure taken;
SDP2B Industry Closures
 1 = Suspension or cancellation recommended
 2 = Suspension or cancellation enacted, but sector-targeted 3 = Suspension or 0 = Geographically-targeted
 cancellation enacted, all levels 1 = State-wide
 0 = No measure taken
SDP3 Restictions on Public Gatherings
 1 = Suspension or closure recommended 2 = Suspension or closure enacted
 0 = Geographically-targeted
 0 = No measure taken
 1 = State-wide
 1 = Restrictions on very large gatherings (the limit is above 1000 people) 2 =
SDP4 Restrictions on Private Gatherings Restrictions on gatherings between 101-1000 people
 3 = Restrictions on gatherings between 11-100 people 4 = Restrictions on
 gatherings of 10 people or less 0 = Geographically-targeted
 1 = State-wide

SDPS Social Distancing Policy Stringency Index 0-100 (rescaled)

Source: CGRT-BRFED (2020).

 7

It is important to note that these mobility measures are not equivalent to the general
population’s behavior. The data capture the behavior of a sub-sample of smartphone users, and
they do not represent the significant share of the population who do not own smartphones, which
is larger in the poorer states of the Brazilian federation. In addition, January and February are
summer vacation months in Brazil. There are reasons to suspect that mobility patterns are lower
due to schools not being in session in these months or only recently restarting. Due to this feature,
the data on the change in distance traveled, and change in non-essential trips may suffer from
measurement error. These measures should also not be interpreted as capturing individual
adherence to social distancing practices, such as mask use, hand-washing, and maintaining at least
2 meters of distance from others in public spaces.
 We also created dummy variables for key national political events, holidays and the days of
the week. We include a dummy for the carnaval, Easter, Tiradentes, Mother’s day, and Workers’
day holidays, a dummy variable for the day of the week, and a time trend. Besides, we include a
variable coded as equal to one for those dates following the evening in which the President
delivered a nation-wide address during the pandemic addressing COVID-19. Finally, we include a
dummy variable for April 16, 2020, which is when President Bolsonaro fired the Minister of
Health, Luiz Mandetta, and for May 15th, 2020, which is the date the second Health Minister,
Nelson Teich, resigned.
 The dynamic time-series cross-section model can be summarized as10:

 Compliancei,t = α0 + α1Compliancei,t−1 + 1SDPSi,t + 2SDPSi,t−1 + 3Eventst + εi,t
 (1)

3. STATE-LEVEL SOCIAL DISTANCING POLICIES

The Social Distancing Policy Stringency (SDPS) Index of the CGRT-BRFED confirms the strong
role states played in mobilizing quickly and collectively to adopt social distancing policies in mid-
March throughout the Brazilian federation. States adopted social distancing measures to close a
wide array of activities, including schools (SDP1), commerce and industry (SDP2A and SDP2B),
and to restrict public gatherings (SDP3), and private events (SDP4). In some states, policies were
adopted in consonance with the first weeks of the epidemic. However, in other states, governments

10
 The unit root tests indicate that our main dependent variables does not contain a unit root process. We applied the
Fisher test for panel unit roots which perform Augmented Dickey-Fuller and Phillips- Perron approaches to test for
stationary processes.

acted preemptively, adopting more stringent social distancing measures in advance of a more
widespread outbreak. However, no state implemented what is equivalent to a full lock-down
containment strategy (Barberia et al., 2020b). Thus, though there is strong evidence of across
country stringency increases, there is significant heterogeneity depending on the specific state and
the specific phase of the epidemic.
 Figure 1 depicts the evolution in the level of the Social Distancing Policy Stringency index
in each Brazilian state in the twelve weeks from March 6 to May 23, 2020. The darkest colored
states in each map are the states with the highest intensity in the stringency level of social
distancing in that specific week.

 Figure 1
 Social Distancing Policy Stringency (SDPS) Index by State from March 6 to May 23, 2020

 Source: CGRT-BRFED (2020).

 By comparing across these weekly figures, we are able to view how the stringency of social
distancing increased within and across states over time. As Figure 1 confirms, Social Distancing
Policy Stringency measures were only in place in three states: Goiás, São Paulo and Rio de Janeiro
by March 13, 2020. This is two weeks after the first case was confirmed in the state of São Paulo,
and by when there were 25 positive cases already confirmed by the Ministry of Health. By March
20, 2020 there was a rapid and exponential rise in the SDPS index across all states in Brazil. In
other words, nearly four weeks after the onset of the pandemic, there was no state that had not

responded by introducing some type of social distancing policies to halt the transmission of
coronavirus. By March 27, 2020, there were 3,477 cases across Brazil and by this date, we can see
there is a marked response in increased social distancing measures being adopted. The states with
the most stringent response were Goiás and Ceará in the week of March 15, even though the
epicenter of the pandemic remained in São Paulo state throughout this period.

 Figure 2
 Social Distancing Policy Stringency (SDPS) Index in Sao Paulo, March 1-May 23, 2020

 Source: CGRT-BRFED (2020).

Figure 2 provides an overview of the timing and stringency level of the policies adopted in the
state of São Paulo. The richest and most populous state of Brazil, São Paulo was the pandemic’s
Brazilian epicenter from March to May 2020. São Paulo issued orders to mandate school closures
by March 16. Commercial activities were also closed, but industry closures were never issued.
There were no mandates issued to prohibit public gatherings; the state’s government issued only
recommendations, and no restrictions were placed on private gatherings. Although it was a pioneer
in the early adoption of non-pharmaceutical interventions, the state ended the month of May being
among the least stringent states in the federation. By then, there were already announcements of
the further lifting of measures in the coming weeks, especially in commerce and activities
regulations, that were the object of protestations by private parties. Even though, the alarming fact
remains that the state led in daily cases and deaths in this period.

Figure 3
 Social Distancing Policy Stringency (SDPS) Index in States with Highest Deaths per 100 Thousand, March 1-
 May 20, 2020

 Source: CGRT-BRFED (2020).

 Figure 4
 Social Distancing Policy Stringency (SDPS) Index in States with Lowest Deaths per 100 Thousand, March 1-
 May 23, 2020

 Source: CGRT-BRFED (2020).

 Figure 3 plots the evolution in the Social Distancing Policy Stringency (SDPS) Index for the
four states with the highest number of deaths per 100,000 people. This figure highlights that the
state of Rio de Janeiro was the first of these to react. Ceará adopted the strictest measures as it was
the only to close industries. There is considerable heterogeneity in the measures adopted by each

of the four states, especially in Rio, where there were many policy changes.
 Figure 4 shows the Social Distancing Policy Stringency for the four states with the lowest
number of deaths per 100,000 people. In an earlier stage of the epidemic, Mato Grosso do Sul had
few cases and adopted few policies. It was the Brazilian state with the lowest stringency score in
this period. Goiás, on the other hand, has been very active in mandating social distancing and,
for some time, was the state with some of the most stringent policies. However, the state began to
ease policies in mid-April.

4. RESULTS

In this section, we use time-series cross-sectional analysis to examine the impact of the level of
stringency of the social distancing policies adopted by Brazilian states to examine whether
mobility depends on the rigidity of the types of measures enacted. We estimate autoregressive
distributed lag models (ARDL) to examine the impact of social distancing policies on mobility
(Equation 1). Our dependent variable is mobility. We employ three different measures: home
permanence, non-essential trips, and distance traveled. Our key independent variable is a
composite measure of social distancing policies.
 We estimate the impact of the SDPS index and the separate effect of each of the six policies
(school closures, commerce and service-sector closures, industry closures, public gathering
restrictions, and private gathering restrictions) as described in Table 1. The estimation results for
the ARDL models are presented in Table A311.
 To assess our hypothesis that more rigid social distancing policies led to lower mobility, we
calculate and test the statistical significance of the short and long-term effects of the social
distancing policies on quarantine compliance. The estimated coefficient on each policy variable in
levels is used to calculate the long-term impact (De Boef & Keele, 2008). We are mainly interested
in the long-term effects of the social distancing policy variables, as these show how compliance to
quarantine is permanently affected after an increase in social distancing policies.
 The results reported in Table A3 show that social distancing policy stringency (SDPS) is a
strong predictor of state-level mobility. Social distancing policies had a positive short-term impact
on the percentage of cellphones at the same geographical location as they were during work hours

11
 For robustness checks, we estimated models controlling for possible omitted variables. The results are presented
in the appendix. Table A4 shows the coefficients for controlling for Bolsonaro 2018 vote share as a proxy for political
ideology and the coefficients for state fixed-effects models. Table A5 presents the results when we control for daily
deaths and daily cases. Figure A1 shows the long-term effects of School Closures, Industry Closures, Commerce
Closures, Public Gatherings Prohibitions, and Private Gathering Prohibitions as well as the long-term coefficients
for the Social Distancing Pol icy Index for all the models employed in robustness checks.

as they were in non-work hours (10 p.m. to 6 a.m.). When sub-national governments adopt the
highest-level social distancing policy stringency (SDPS), there is an expected short-run impact of
4.86% (or between
 2.54 and 7.18% using a 95% confidence interval) on home permanence (column 2). The
expected short-run impact of rigorous social distancing policies (SDPS = 100) is a reduction of
19% (or between -0.27 and -0.11 using a 95% confidence interval) on the average trips to non-
essential locations (column 4). Finally, stringent social distancing policies (SDPS = 100) produce
a reduction of 0.11 (or between -0.15 and -0.06 using a 95% confidence interval) on the average
kilometers driven by an individual in a specific day in a state (column 6). Nonetheless, up to now,
among all Brazilian states, only Goiás (from April 5 to April 18, 2020) enacted rigorous policies
and achieved the highest level of stringency in the SDPS due to these four closure policies (SDPS
= 100).
 The short-run impacts of each policy on quarantine compliance measures underscore that
there are benefits from changing a single policy’s stringency. When states suspend or cancel classes
in all schools in that place (S1 = 4), our results suggest that such a measure produces an immediate
increase of 2.08% (or between 0.89 and 3.27 using a 95% confidence interval) in the share of
people that stayed home (column 1). A strict policy of school closures also produces an immediate
decrease of 0.09% (or between -0.13 and -0.06 using a 95% confidence interval) on trips for non-
essential services (column 3) and a reduction of 0.04 (or between -.08 and -0.01 using a 95%
confidence interval) on the average distance traveled (column 5). Besides school closures, a strict
policy of prohibition of public gatherings reduces in 0.08% (or between -0.13 and -0.04 using a
95% confidence interval) the trips to non-essential services (column 3). Commerce and service
closures also produce an immediate impact on quarantine compliance. A strict policy on commerce
and service closures reduces in 0.06 (or between -0.08 and -0.02 using a 95% confidence interval)
the average kilometers undertaken on a specific day in a state (column 5). All else equal, our results
suggest that the changes in other policies did not have a statistically significant short-run impact
on quarantine compliance measures.

Figure 5
 The Impact of Social Distancing Policies on Home Permanence

 Source: Elaborated by the authors.

 Figure 6
The Impact of Social Distancing Policies on the Change in Non-Essential Trips from Baseline

 Source: Elaborated by the authors.

Figure 7
 The Impact of Social Distancing Policies on the Change in Distance Traveled from the Baseline

 Source: Elaborated by the authors.

 Figure 5 depicts the parameter estimates and the 95% confidence intervals for the long-
term effects for social distancing policies reported in Table A3. There are long-term effects on the
percentage of people that stay at home when governments enact school closure policies, commerce
and services closure policy, and public gathering prohibitions. However, the long-run impact is
substantively large when states im- plement all policies together. Rigorous social distancing
policies (SDPS = 100) in- creases the percentage of people that stayed at home by 21.70% (or
between 18.47% and 24.94% using a 95% confidence interval).
 Figure 6 depicts the long-term effects of social distancing policies on the change in non-
essential trips. A package of stringent social distancing policies produces a reduction of 0.76% (or
between -0.87 and -0.65 using a 95% confidence interval) in the trips made for non-essential
services relative to the baseline.
 Figure 7 shows the long-term effects of social distancing policies on the change in average
distance traveled in the state. As in the case of the other compliance measures analyzed, a package
of rigorous social distancing policies reduces the average kilo- meters traveled in a day by 0.52
(or between -0.62 and -0.43 using a 95% confidence interval) relative to the baseline.

5. CONCLUSION

The goal of this study was to contribute to the understanding of the effectiveness of social
distancing policy measures to contain the further spread of COVID-19 across Brazil’s 26 states
and the federal district in the early pandemic stage. By providing a comparable measure of the
stringency of social distancing policies adopted by state governments over time, our results
highlight that state government were instrumental in Brazil’s efforts to slow down the transmission
rate. First, social distancing measures were enacted across a wide range of activities by state
governments in the weeks immediately after the first cases were detected. Second, some states
stand out for their proactive adoption of stringent social distancing policies before a widespread
outbreak. Finally, states introduced social distancing measures and kept these measures in place
for several weeks.
 As shown in the analysis, each non-pharmaceutical policy contributed to the mobility of
specific groups across states. Consequently, holding the remaining policies constant, school
closures (SDP1), commerce and industry closures (SDP2), restrictions on public and private
gatherings (SDP3 and SDP4) affected social distancing. Moreover, the adoption of coherent policy
sets that include, preferably, a wide range of policy areas had a bigger impact on home permanence.
 As the pandemic continued to exact a decisive and deathly impact across Brazil,
nevertheless, our study also confirms that these policies were relatively moderate. Similarly, the
permanence at home peaked in the last weeks of March. Since then, even though non-
pharmaceutical intervention policies remained in place, mobility patterns gradually increased. Our
results show that although mobility patterns did not re- turn to pre-pandemic levels, significant
increases took place. Furthermore, the pace of this movement in mobility can be linked with the
policies adopted by state governments. By allowing commerce, services, and industry to remain
open, consumers are shopping, and workers traveled to work. Our results suggest that the
effectiveness of non-pharmaceutical interventions to increase social distancing is stronger when
comprehensive measures are put into place and sustained over time. Small changes in specific non-
pharmaceutical interventions, holding all else equal, had a limited impact on mobility.

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Lorena G. Barberia

 https://orcid.org/0000-0001-6319-543X
Ph.D. in Government and Associate Professor in Department of Political Science at University of São Paulo.
E-mail: lorenabarberia@usp.br

Luiz G. R. Cantarelli

 https://orcid.org/0000-0002-7735-7775
Master’s Degree Candidate in Department of Political Science, University of São Paulo. E-mail:
luiz.cantarelli@usp.br

Maria Leticia Claro de Faria Oliveira

 https://orcid.org/0000-0003-1305-3278
Master’s Candidate in Department of Political Science, University of São Paulo. E-mail:
maria.leticia.oliveira@usp.br

Natália de Paula Moreira

 https://orcid.org/0000-0002-8544-1133
Ph.D. Candidate in Political Science at University of São Paulo. E-mail: natalia.pmoreira@usp.br

Isabel Seelaender Costa Rosa

 https://orcid.org/0000-0001-8480-5196
Bachelor’s Degree in Social Science at University of São Paulo. E-mail: isabel.rosa@usp.br

APPENDIX

 Table A1
 CGRT-BRFED Summary Statistics

 (1) (2) (3) (4) (5)
Variables N mean sd min max

State 2,592 14 7.790 1 27
Date 2,592 21,963 27.72 21,915 22,010
Deaths 2,592 1.058 11.14 0 260
New Cases 2,592 4.043 29.43 0 822
Total Cases 2,592 32.26 206.4 0 4,466
Region 2,592 2.889 1.618 1 5
Log of Cases 648 3.138 1.910 0 8.404
Log of Deaths 206 1.256 1.340 0 5.561
School Closure 2,592 0.389 0.784 0 2
School Closure Geographic Coverage 2,592 0.188 0.391 0 1
Commerce Closure 2,592 0.324 0.733 0 2
Commerce Closure Geographic Coverage 2,592 0.145 0.353 0 1
Industry Closure 2,592 0.0394 0.271 0 2
Industry Closure Geographic Coverage 2,592 0.0177 0.132 0 1
Public Gatherings 2,592 0.367 0.757 0 2
Public Gatherings Geographic Coverage 2,592 0.188 0.391 0 1
School Closures Index 2,592 0.577 1.163 0 3
Workplace Closures - Commerce Index 2,592 0.470 1.066 0 3
Workplace Closures - Industry Index 2,592 0.0571 0.400 0 3
Public Events Closures - Index 2,592 0.555 1.134 0 3
Social Distance Index 2,592 1.658 3.345 0 12
Social Distance Index 2,592 13.82 27.87 0 100
Day of first case 2,592 0.0104 0.102 0 1
Day of first death 2,592 0.00965 0.0978 0 1
School Day Closures 2,592 0.0100 0.0997 0 1
Commerce Day Closures 2,592 0.00926 0.0958 0 1
Industry Day Closures 2,592 0.00116 0.0340 0 1
Public Day Closures 2,592 0.00926 0.0958 0 1

 Source: Elaborated by the authors.

Figure A1
Long-run effects of the additional models estimated for robustness checks

 Source: Elaborated by the authors.

Table A2
First COVID-19 Infection and Reported Death in each State and the Federal District, Brazil

 State First Case First Death
 AC 17mar2020 06apr2020
 AL 08mar2020 31mar2020
 AM 13mar2020 24mar2020
 AP 20mar2020 04apr2020
 BA 06mar2020 29mar2020
 CE 15mar2020 26mar2020
 DF 07mar2020 29mar2020
 ES 05mar2020 02apr2020
 GO 12mar2020 26mar2020
 MA 20mar2020 30mar2020
 MG 08mar2020 30mar2020
 MS 14mar2020 31mar2020
 MT 20mar2020 03apr2020
 PA 18mar2020 02apr2020
 PB 18mar2020 01apr2020
 PE 12mar2020 25mar2020
 PI 19mar2020 28mar2020
 PR 12mar2020 27mar2020
 RJ 05mar2020 19mar2020
 RN 12mar2020 29mar2020
 RO 19mar2020 31mar2020
 RR 21mar2020 04apr2020
 RS 10mar2020 25mar2020
 SC 12mar2020 26mar2020
 SE 14mar2020 02apr2020
 SP 25feb2020 17mar2020
 TO 18mar2020 15apr2020
 Source: Elaborated by the authors.

Table A3
 The Effect of Social Distancing Policies on Quarantine Compliance
 (1) (2) (3) (4) (5) (6)
 Home permanencet Home permanencet Non Essentialt Non Essentialt Dist Traveledt Dist Traveledt
Home permanencet−1 0.73∗∗∗ 0.77∗∗∗
 (0.01) (0.02)
Non Essentialt−1 0.81∗∗∗ (0.02) 0.85∗∗∗ (0.02)

Distance Traveledt−1 0.78∗∗∗ (0.02) 0.81∗∗∗ (0.02)

School Closuret 0.52∗∗∗ (0.15) -0.02∗∗∗ (0.00) -0.01∗∗ (0.00)

Industry Closuret 0.43 -0.01 -0.01
 (0.50) (0.01) (0.01)
Commerce Closuret 0.41 -0.01 -0.01∗∗∗
 (0.25) (0.01) (0.00)
Public Gathering Proh.t 0.29 -0.03∗∗∗ -0.01
 (0.23) (0.01) (0.01)
Private Gathering Proh.t -0.04 0.01 0.00
 (0.17) (0.00) (0.00)
School Closuret−1 0.00 0.01∗ 0.00
 (0.17) (0.00) (0.00)
Industry Closuret−1 -0.48 0.01 0.01
 (0.54) (0.01) (0.01)
Commerce Closuret−1 -0.03 0.00 0.00
 (0.22) (0.01) (0.00)
Public Gathering Proh.t−1 0.45∗ (0.23) 0.01 -0.00
 (0.01) (0.01)
Private Gathering Proh.t−1 -0.07 -0.01 -0.00
 (0.17) (0.01) (0.00)
Social Distancing Policiest 4.86∗∗∗ (1.13) -0.19∗∗∗ (0.04) -0.11∗∗∗
 (0.02)
Social Distancing Policiest−1 0.10 0.08∗∗ 0.01
 (1.09) (0.03) (0.02)
President Speech 0.55∗∗∗ (0.12) 0.50∗∗∗ (0.12) -0.02∗∗∗ (0.00) -0.02∗∗∗ (0.00) 0.01∗∗∗ (0.00) 0.01∗∗∗ (0.00)

First death 0.02 0.10 -0.01 -0.01 0.01 0.01
 (0.45) (0.45) (0.01) (0.01) (0.01) (0.01)
First case 1.80∗∗∗ 1.64∗∗∗ -0.04∗∗ -0.04∗ -0.01 -0.01
 (0.37) (0.37) (0.02) (0.02) (0.01) (0.01)
First deatht−1 0.73∗∗ 0.81∗∗ -0.02∗∗ -0.02∗∗ -0.03∗∗ -0.03∗∗
 (0.34) (0.33) (0.01) (0.01) (0.01) (0.01)
First caset−1 1.98∗∗ (0.73) 1.88∗∗ (0.81) -0.07∗∗∗ (0.02) -0.07∗∗∗ (0.02) -0.02 -0.02
 (0.01) (0.01)
Time trend -0.01 0.00 0.00 -0.00 0.00∗∗∗ (0.00) 0.00∗∗∗ (0.00)
 (0.00) (0.00) (0.00) (0.00)
 N 2997 2997 2997 2997 2997 2997
R2 0.911 0.908 0.949 0.948 0.897 0.895

 Note: standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
 The output for days of the week, holidays, and changes in the Health Ministry are omitted.
 Source: Elaborated by the authors.

Table A4
 Controlling for Time-Invariant Variables

 (1) (2) (3) (4)
 Home Permanencet Home Permanencet Home Permanencet Home Permanencet
Home Permanencet 1 − 0.73∗∗∗ 0.77∗∗∗ 0.67∗∗∗ 0.70∗∗∗
 (0.01) (0.02) (0.02) (0.02)
School Closuret 0.52∗∗∗ 0.63∗∗∗
 (0.15) (0.16)
Industry Closuret 0.43 0.54
 (0.50) (0.56)
Commerce Closuret 0.41 0.45∗
 (0.25) (0.26)
Public Gathering Proht 0.29 0.34
 (0.23) (0.23)
Private Gathering Proht -0.04 -0.07
 (0.17) (0.19)
School Closuret 1 0.00 0.14
 −
 (0.17) (0.17)
Industry Closuret 1 -0.48 -0.45
 −
 (0.53) (0.51)
Commerce Closuret 1 -0.03 0.03
 −
 (0.22) (0.21)
Public Gathering Proht 1 0.45∗ 0.52∗∗
 −
 (0.23) (0.23)
Private Gathering Proht 1 -0.07 -0.12
 −
 (0.17) (0.16)
Social Distancing Policiest 4.86∗∗∗ 5.89∗∗∗
 (1.13) (1.16)
Social Distancing Policiest 1 0.10 1.27
 −
 (1.08) (0.98)
Bolsonaro Vote Sharet 0.00 0.00
 (0.01) (0.01)
President Speecht 0.55∗∗∗ 0.50∗∗∗ 0.68∗∗∗ 0.62∗∗∗
 (0.12) (0.12) (0.13) (0.11)
Time trend -0.01 0.00 -0.01∗ -0.01
 (0.00) (0.00) (0.01) (0.00)
First Caset 1.80∗∗∗ 1.64∗∗∗ 1.90∗∗∗ 1.84∗∗∗ (0.38)
 (0.37) (0.38) (0.37)
First Caset 1 1.98∗∗ 1.88∗∗ 2.08∗∗∗ 2.04∗∗ (0.73)
 −
 (0.81) (0.70) (0.78)
First Deatht 0.02 0.10 0.19 0.27
 (0.45) (0.45) (0.46) (0.46)
First Deatht 1 0.73∗∗ 0.81∗∗ 0.85∗∗ 0.91∗∗ (0.34)
 −
 (0.33) (0.35) (0.35)
State Fixed-Effects No No Yes Yes
N 2997 2997 2997 2997
R2 0.911 0.908 0.915 0.912

Note: standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
The output for days of the week, holidays, and changes in the Health Ministry are omitted.
Source: Elaborated by the authors.

Table A5
 Controlling for Daily New Cases and New Deaths

 (1) (2)
 Home Permanencet Home Permanencet
Home Permanencet-1 0.73∗∗∗ 0.76∗∗∗
 −
 (0.02) (0.02)
School Closuret 0.09
 (0.16)
Industry Closuret 0.27
 (0.47)
Commerce Closuret 0.37
 (0.23)
Public Gathering Proht 0.24
 (0.23)
Private Gathering Proht -0.08 (0.14)
School Closuret 1 − 0.27
 (0.19)
Industry Closuret 1 -0.34
 −
 (0.50)
Commerce Closuret 1 0.01
 −
 (0.21)
Public Gathering Proht 1 −
 0.24
 (0.24)
Private Gathering Proht 1 0.06
 −
 (0.14)
Social Distancing Policyt 2.19∗
 (1.18)
Social Distancing Policyt 1 1.52
 −
 (1.06)
President Speecht -0.27∗ -0.31∗∗
 (0.14) (0.13)
Time trend -0.04∗∗∗ -0.04∗∗∗
 (0.01) (0.01)

New Deathst 0.00 0.00
 (0.00) (0.00)
 New Deathst 1 -0.00∗ -0.00
 −
 (0.00) (0.00)
 New Casest 0.00∗∗ 0.00∗∗∗
 (0.00) (0.00)
 New Casest 1 0.00 0.00∗
 −

 (0.00) (0.00)
 N 1914 1914
 R2 0.825 0.821
Note: standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.
The output for days of the week, holidays, and changes in the Health Ministry are omitted.
Source: Elaborated by the authors.